The problem of data sparsity largely limits the accuracy of recommender systems in collaborative filtering model. To
alleviate the problem, cross-domain collaborative filtering was proposed by harnessing the information from the auxiliary domains.
Previous works mainly focused on improving the model of utilizing the auxiliary information yet little on the selection of auxiliary
domains, although it is observed that the result of recommendation depends on the characteristics of auxiliary dataset. In this paper, we
study the validity of cross-domain collaborative filtering by movie recommendation via different auxiliary domains of different movie
genres. Through extensive experiments we find that the number of overlapping users between target domain and auxiliary domain is
an indicator of choosing beneficial domains, while the low Kullback-Leibler divergence between non-overlapping user ratings, rather
than the overlapping user ratings, is much more significant. The results are helpful in selection of auxiliary domains in cross-domain
collaborative filtering.